Multimedia Tools and Applications

, Volume 76, Issue 24, pp 26249–26271 | Cite as

Anomaly detection using sparse reconstruction in crowded scenes

  • Ang LiEmail author
  • Zhenjiang Miao
  • Yigang Cen
  • Yi Cen


In this paper, we propose an algorithm of anomaly detection in crowded scenes by using sparse representation over the normal bases. First, the histogram of maximal optical flow projection (HMOFP) features are extracted from a set of normal training data. Then, the online dictionary learning algorithm is used to train an optimal dictionary with proper redundancy, which is better than the dictionary simply composed by the HMOFP features of the whole training data. In order to detect the normalness of a frame, the l 1-norm of the sparse reconstruction coefficients is used as the Reconstruction Coefficient Sparsity (RCS). Our algorithm is effective for both global abnormal events (GAE) and local abnormal events (LAE). We evaluate our method on three benchmark datasets-the UMN dataset, the PETS2009 dataset and the UCSD Ped1 dataset. Compared with the most popular methods, experimental results show that our algorithm achieves good results especially for the pixel-level local abnormal event localization.


HMOFP Online dictionary learning Sparse representation Abnormal events Crowded scenes 



This work is supported by the 973 Program (no. 2011CB302203), NSFC (nos. 61572067, 61272028, 61273274, 61672089, 61602538, and 61572064), PXM2016_014219_000025, National Key Technology R&D Program of China (no. 2012BAH01F03), NSFB (no. 4123104), Beijing Municipal Natural Science Foundation (no. 4162050), and Natural Science Foundation of Guangdong Province (no. 2016A030313708).


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Institute of Information ScienceBeijing Jiaotong UniversityBeijingChina
  2. 2.Beijing Key Laboratory of Advanced Information Science and Network TechnologyBeijingChina
  3. 3.School of Information EngineeringMinzu University of ChinaBeijingChina

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